scholarly journals Simulation and Implementation of a Modified ANFIS MPPT Technique

2020 ◽  
Author(s):  
Bachar Meryem ◽  
Naddami Ahmed ◽  
Fahli Ahmed
Keyword(s):  
Fuzzy Inference ◽  
Experimental Tests ◽  
Optimal Operation ◽  
Climatic Conditions ◽  
Buck Converter ◽  
Maximum Power ◽  
Pv System ◽  
Maximum Power Point ◽  
Inference System ◽  
Power Point

The maximum power point tracking (MPPT) algorithms ensure optimal operation of a photovoltaic (PV) system to extract the maximum PV power, regardless of the climatic conditions. This paper exposes the study, design, simulation and implementation of a modified advanced neural fuzzy inference system (ANFIS) MPPT algorithm based on fuzzy data for a PV system. The studied system includes a PV array, a DC/DC buck converter, the ANFIS controller, a proportional-integral (PI) controller, and a load. The simulation and experimental tests are carried out with the MATLAB/Simulink software and LabVIEW, respectively. Moreover, the obtained results are compared with previously published results by incremental conductance (IC) and fuzzy logic (FL) algorithms under different climatic conditions of irradiation and temperature. The results show that the proposed ANFIS algorithm is able to track the maximum power point for varying climatic conditions. Furthermore, the comparison analysis reveals that the PV system using ANFIS algorithm has more efficient and better dynamic response than FL and IC.

scholarly journals Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 858 ◽  
Author(s):  
Sadeq D. Al-Majidi ◽  
Maysam F. Abbod ◽  
Hamed S. Al-Raweshidy
Keyword(s):  
Fuzzy Inference ◽  
Experimental Tests ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Training Data ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Inference System ◽  
Photovoltaic Array ◽  
Power Point

Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this paper, an ANFIS-MPPT method based on a large experimental training data is designed to avoid the system from experiencing a high training error. Those data are collected throughout the whole of 2018 from experimental tests of a photovoltaic array installed at Brunel University, London, United Kingdom. Normally, data from experimental tests include errors and therefore are analyzed using a curve fitting technique to optimize the tuning of ANFIS model. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. A real measurement test of a semi-cloudy day is used to calculate the average efficiency of the proposed method under varying climatic conditions. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.

scholarly journals Improved strategy of an MPPT based on the sliding mode control for a PV system

2020 ◽  
Vol 10 (3) ◽  
pp. 3074
Author(s):  
Taouni Abderrahim ◽  
Touati Abdelwahed ◽  
Majdoul Radouane
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
Climatic Conditions ◽  
Buck Converter ◽  
Maximum Power ◽  
Weather Factors ◽  
Pv System ◽  
Maximum Power Point ◽  
Mode Control ◽  
Power Point

The energy produced using a photovoltaic (PV) is mainly dependent on weather factors such as temperature and solar radiation. Given the high cost and low yield of a PV system, it must operate at maximum power point (MPP), which varies according to changes in load and weather conditions. This contribution presents an improved maximum power point tracking (MPPT) controllers of a PV system in various climatic conditions. The first is a sliding mode MPPT that designed to be applied to a buck converter in order to achieve an optimal PV array output voltage. The second MPPT is based on the incremental conductance algorithm or Perturb-and-Observe algorithm. It provides the output reference PV voltage to the sliding mode controller acting on the duty cycle of the DC-DC converter. Simulation is carried out in SimPower toolbox of Matlab/Simulink. Simulation results confirm the effectiveness of the sliding mode control MPPT under the parameter variation environments and shown that the controllers meet its objectives.

Performance Evaluation of Various Adaptive Neuro Fuzzy Inference System Based Maximum Power Point Tracking for Photovoltaic System

2015 ◽  
Vol 785 ◽  
pp. 215-219
Author(s):  
Ammar Hussein Mutlag ◽  
Hussein Shareef ◽  
Azah Mohamed ◽  
Jamal Abd Ali ◽  
Maytham S. Ahmed
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Inference System ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Neuro Fuzzy ◽  
Power Point

The maximum output power of a photovoltaic (PV) system with a DC-DC converter depends mainly on the solar irradiance (G) and the temperature (T). Therefore, a maximum power point tracking (MPPT) mechanism is required to improve the overall system. The conventional MPPT approaches such as the perturbation and observation (P&O) technique have difficulty in finding true maximum power point. Thus various intelligent MPPT systems such as fuzzy logic controllers (FLC) are recently introduced. In FLC based MPPT, selecting the type of the membership function (MF) and the number of the fuzzy sets (FS) is critical for better performance. Thus, in this paper various adaptive neuro fuzzy inference system (ANFIS) is utilized to automatically tune the FLC membership functions instead of adopting the trial and error method. To find suitable MF for FLC, ANFIS is developed in MATLAB/Simulink and the effect of different types MF investigated. Simulation result shows that the FLC with triangular MF and seven FS give the best result. The evaluation indices used in this study includes the maximum extracted energy, minimum standard deviation of the error, and minimum mean square error.

scholarly journals Comparative Study of Maximum Power Point Tracking Techniques for Photovoltaic Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Fernando Lessa Tofoli ◽  
Dênis de Castro Pereira ◽  
Wesley Josias de Paula
Keyword(s):  
Comparative Study ◽  
Fossil Fuels ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Photovoltaic Systems ◽  
Pv System ◽  
Maximum Power Point ◽  
Pv Systems ◽  
Point Tracking ◽  
Power Point

The generation of electricity from photovoltaic (PV) arrays has been increasingly considered as a prominent alternative to fossil fuels. However, the conversion efficiency is typically low and the initial cost is still appreciable. A required feature of a PV system is the ability to track the maximum power point (MPP) of the PV array. Besides, MPP tracking (MPPT) is desirable in both grid-connected and stand-alone photovoltaic systems because the solar irradiance and temperature change throughout the day, as well as along seasons and geographical conditions, also leading to the modification of theI×V(current versus voltage) andP×V(power versus voltage) curves of the PV module. MPPT is also justified by the relatively high cost of the energy generated by PV systems if compared with other sources. Since there are various MPPT approaches available in the literature, this work presents a comparative study among four popular techniques, which are the fixed duty cycle method, constant voltage (CV), perturb and observe (P&O), and incremental conductance (IC). It considers different operational climatic conditions (i.e., irradiance and temperature), since the MPP is nonlinear with the environment status. PSIM software is used to validate the assumptions, while relevant results are discussed in detail.

scholarly journals Power optimization in partially shaded photovoltaic systems

2018 ◽  
Vol 12 (1) ◽  
pp. 34-38
Author(s):  
Halil Erol ◽  
Mahmut Uçman
Keyword(s):  
Climatic Conditions ◽  
Maximum Power ◽  
Hill Climbing ◽  
Solar Pv ◽  
Pv System ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Point Tracking ◽  
Linear Behaviour ◽  
Power Point

The Power-Voltage characteristic of a photovoltaic (PV) array exhibits non-linear behaviour when exposed to uniform solar irradiance. Maximum Power Point (MPP) tracking is challenging due to the varying climatic conditions in a solar PV system. Moreover, the tracking algorithm becomes more complicated due to the presence of multiple peaks in the power voltage characteristics under the condition of partial shading. This research is devoted to the Stochastic Beam Search (SBS) based algorithm and Stochastic Hill Climbing (SHC) for a maximum power point tracking (MPPT) at a partial shading condition in the PV system. To give a partial shading effect over the entire array of a PV system, a mast is placed in front of the modules. The modules in the array are connected in such a way that one does not need to rewire the electrical connection during the rearrangement of modules. It is validated that the power generation performance of an array under a moving shading condition is increased. Furthermore, it is observed that the SHC method outperforms the SBS method in the MMP tracking.

scholarly journals Desain dan Pemodelan Maximum Power Point Tracking Menggunakan ANFIS pada Sistem Photovoltaic dengan Buckboost Converter

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Abil Huda ◽  
Patria Julianto
Keyword(s):  
Energy Source ◽  
Fuzzy Inference ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Inference System ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Neuro Fuzzy ◽  
Power Point

AbstrakKebutuhan energi listrik semakin hari semakin meningkat. Bahan bakar fosil seperti batu bara, gas dan minyak bumi merupakan energi yang tidak dapat diperbaharui serta memiliki jumlah yang terbatas. Hal itu mendorong berkembangnya energi terbarukan sebagai sumber energi alternatif pembangkit tenaga listrik. Salah satu sumber energi terbarukan adalah sel surya atau photovoltaic (PV) yang memanfaatkan energicahaya matahari. Permasalahan dalam penggunaan PV saat ini adalah efisiensinya yang masih rendah dengan biaya yang tinggi. Efisiensi PV dapat ditingkatkan menggunakan metode Maximum Power Point Tracking (MPPT) untuk menghasilkan daya maksimum PV. Penelitian tentang MPPT dilakukan dengan berbagai metode pada aplikasi sistem PV menggunakan Buckboost converter. Penelitian ini menggunakan Adaptive Neuro Fuzzy Inference System (ANFIS) sebagai MPPT untuk menjejak titik daya maksimum.Hasil penelitian menunjukkan bahwa ANFIS mampu bekerja sebagai MPPT dan memiliki respon yang baik dalam menjejak titik daya maksimum diberbagai kondisi iradiasi dan temperatur. PV menghasilkan daya maksimum saat temeperatur tetap dengan variasi iradiasi 400 W/m2, 600 W/m2, 800 W/m2 dan 1000 W/m2. PV juga menghasilkan daya maksimum saat iradiasi tetap dengan variasi temperatur 15oC, 25 oC dan 40 oC.Kata Kunci: ANFIS, Buckboost, MPPT, Photovoltaic.AbstractThe need for electric energy is increasing every day. Fossil fuels such as coal, gas and petroleum are nonrenewable energy and have a limited amount. It encourages the development of renewable energy as an alternative energy source of electricity. One renewable energy source is a solar cell or photovoltaic (PV) that utilizes solar light energy. The problem with current PV usage is its low efficiency with high cost. The PV efficiency can be improved using the Maximum Power Point Tracking (MPPT) method to generatemaximum PV power. Research on MPPT is done by various methods on PV system application using Buckboost converter. This research uses Adaptive Neuro Fuzzy Inference System (ANFIS) as MPPT to track maximum power point. The results show that ANFIS is able to work as MPPT and has a good response in tracking the maximum power point in various irradiation and temperature conditions. PV produces maximum power during fixed temperature with irradiation variations of 400 W/m2, 600 W/m2, 800 W/m2 and 1000 W/m2. PV also produces maximum power when irradiation is fixed with temperaturevariations 15oC, 25 oC and 40 oC.Keywords: ANFIS, Buckboost, MPPT, Photovoltaic

scholarly journals MPPT Charge Controller Design in a Solar PV System Under Rapidly Changing Climate Condition

2019 ◽  
pp. 1-8
Author(s):  
Mostafizur Rahman ◽  
Md. Mahmudur Rahman
Keyword(s):  
Controller Design ◽  
Computer Software ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Solar Pv ◽  
Pv System ◽  
Maximum Power Point ◽  
Maximum Power Point Tracker ◽  
Power Point

This paper presents a detailed theoretical study of photovoltaic (PV) systems and their operation using the MPPT (Maximum Power Point Tracking) method and presents the simulation of photovoltaic modules validated by computer software simulation followed by an experimental setup of MATLAB R2017a. The first approach to build the performance of a photovoltaic solar panel is to use a maximum power point tracker in rapidly changing climatic conditions and use a DC-DC converter to maximize the output power. This framework can operate at the maximum power point MPP and produces its highest power in different irradiance conditions when the solar panels are partially shaded. The main perspectivesis design and simulation of a simple but efficient charge controller by utilizing maximum power point tracker for photovoltaic system and analysis results show that this MPPT system with perturb & observe (P&O) method and the DCDC Boost converter can significantly increase the efficiency and the performance of PV.

scholarly journals PENGOPTIMALAN ENERGI TERBARUKAN MENGGUNAKAN MAXIMUM POWER POINT TRACKING BERBASIS ADAPTIVE NEURO FUZZY INFERENCE SYSTEM DAN PHOTOVOLTAIC BERBASIS DYE SENTIZED SOLAR CELL (DSSC)

2019 ◽  
Vol 9 (1) ◽  
pp. 95
Author(s):  
Abil Huda Huda ◽  
Hadi Santoso
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Maximum Power Point Tracking ◽  
Maximum Power ◽  
Maximum Power Point ◽  
Inference System ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Neuro Fuzzy ◽  
Power Point

Saat ini bahan bakar fosil berupa minyak bumi dan batu-bara masih menjadi sumber energi yang paling banyak digunakan dalam proses pembangkitan tenaga listrik. Bahan bakar fosil tersebut tidak dapat diperbarui dan jumlahnya semakin menipis seiring dengan berjalannya waktu. Karena kebutuhan energi listrik semakin hari semakin meningkat, sumber energi alternatif terutama sumber energi terbarukan menjadi semakin dibutuhkan. (Sankarganesh, R. & Thangvel).Salah satu sumber energi terbarukan adalah Photovoltaic (PV) yang memanfaatkan energi cahaya matahari. Sumber energi ini memiliki kelebihan yaitu bersih dan tersedia di alam dalam jumlah yang melimpah (Soedibyo, Priananda, C. W. & Haikal, M. A).Sejak ditemukannya PV, teknologi ini terus berkembang hingga saat ini telah ditemukan beberapa jenis sel surya. Pada tahun 1991, seorang ilmuan asal Swis, Michael Gratzel menemukan salah satu jenis sel surya yang memanfaatkan kandungan organik tumbuhan. Temuannya tersebut dikenal dengan Dye Sensitized Solar Cell (DSSC). Prinsip kerja DSSC adalah memanfaatkan eksitasi elektron oleh foton pada cahaya matahari yang mengenai bagian sensitif dari tumbuhan yang disebut dengan dye (O’regan dan Gratzel).Permasalahan dalam penggunaan PV, tak terkecuali jenis DSSC saat ini adalah efisiensinya yang masih rendah dengan biaya yang tinggi. Kebutuhan penggunaan PV yang semakin meningkat mendorong berbagai penelitian mengenai peningkatan efisiensi pada sistem PV. Terdapat tiga faktor yang mempengaruhi efisiensi sistem PV. Pertama adalah material PV. Kedua adalah efisiensi konverter dan efisiensi algoritma Maximum Power Point Tracking (MPPT) (Devi, M. L. & Chilambarasan, M).Adaptive Neuro Fuzzy Inference System (ANFIS) merupakan metode yang menggunakan jaring syaraf tiruan (Neural Network) untuk mengimplementasikan Fuzzy Inference System (FIS) atau sistem inferensi fuzzy. Keunggulan sistem inferensi fuzzy adalah dapat menerjemahkan pengetahuan dari pakar dalam bentuk aturan-aturan, namun biasanya dibutuhkan waktu yang lama untuk menetapkan fungsi keanggotaannya. Oleh sebab itu dibutuhkan teknik pembelajaran dari jaringan syaraf tiruan untuk mengotomatisasi proses tersebut sehingga dapat mengurangi waktu pencarian, hal tersebut menyebabkan metode ANFIS sangat baik untuk diterapkan pada MPPT (Tarek, B., Said, D., & Benbouzid, M.E.H).Penelitian ini mengoptimalkan sistem photovoltaic menggunakan DSSC untuk sisi material, Cuk converter untuk sisi converter dan metode ANFIS sebagai MPPT (Maximum Power Point Tracking) yang mengontrol Cuk converter untuk aplikasi sistem PV.

scholarly journals Comparative Analysis of Maximum Power Point Tracking Controllers under Partial Shaded Conditions in a Photovoltaic System

2015 ◽  
Vol 12 (1) ◽  
pp. 15 ◽  
Author(s):  
R. Ramaprabha ◽  
S.P. Chitra
Keyword(s):  
Maximum Power Point Tracking ◽  
Optimal Operation ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Control Technique ◽  
Pv System ◽  
Maximum Power Point ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Power Point

Mismatching effects due to partial shaded conditions are the major drawbacks existing in today’s photovoltaic (PV) systems. These mismatch effects are greatly reduced in distributed PV system architecture where each panel is effectively decoupled from its neighboring panel. To obtain the optimal operation of the PV panels, maximum power point tracking (MPPT) techniques are used. In partial shaded conditions, detecting the maximum operating point is difficult as the characteristic curves are complex with multiple peaks. In this paper, a neural network control technique is employed for MPPT. Detailed analyses were carried out on MPPT controllers in centralized and distributed architecture under partial shaded environments. The efficiency of the MPPT controllers and the effectiveness of the proposed control technique under partial shaded environments was examined using MATLAB software. The results were validated through experimentation. 

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